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Multimedia Tools and Applications

, Volume 78, Issue 1, pp 1117–1130 | Cite as

A discriminant method of single-optical-axis omnidirectional vision system

  • Peng Tian
  • Huixian DuanEmail author
Article
  • 37 Downloads

Abstract

Generally, due to the instrumental error of omnidirectional camera, it is very difficult to satisfy the standard single-optical-axis request. Thus, it is necessary to evaluate whether a single-optical-axis camera lens is aligned or has tangent distortion. In this paper, we propose a discriminant function of single-optical-axis omnidirectional vision system based on checkerboard, which is only related to the image points and does not involve any camera parameters. Firstly, under single-optical-axis omnidirectional camera, the geometric invariance among image points of collinear and equidistant space points is derived. Next, based on the derived geometric invariance in a single image, we construct the object function to discriminate the standard single-optical-axis omnidirectional camera. Finally, a discriminant method of single-optical-axis omnidirectional vision system is proposed based on the checkerboard. Experimental results on both simulated and real data have demonstrated the usefulness and effectiveness of our method.

Keywords

Single-optical-axis Omnidirectional vision system Cross ratio Projective invariance 

Notes

Acknowledgments

This work is sponsored by the Shanghai Rising-Star Program (17QB1401000); and by Shanghai science and technology innovation action plan(17511108200); and by the National Natural Science Foundation of China (61403084, 61402116); and by the Application Innovation Plan of Ministry of Public Security (2017YYCXSXST030).

References

  1. 1.
    Baker S, Nayer S (1999) A theory of single-viewpoint catadioptric image formation. Int J Comput Vis 35:175–196CrossRefGoogle Scholar
  2. 2.
    Barreto JP, Araujo H (2005) Geometry properties of central catadioptric line images and application in calibration. IEEE Trans Pattern Anal Mach Intell 27:1327–1333CrossRefGoogle Scholar
  3. 3.
    Deng XM, Wu FC, Wu YH (2007) An easy calibration method for central catadioptric cameras. Acta Automat Sin 33:801–808CrossRefGoogle Scholar
  4. 4.
    Dress AWM, Wenzel W (1991) Grassmann-Plücker relations and matroids with coefficients. Adv Math 86:68–110MathSciNetCrossRefGoogle Scholar
  5. 5.
    Duan FQ, Wang L (2010) Calibrating central catadioptric cameras based on spatial line projection constraint. In: International conference on systems, man and cybernetics, pp 2088–2093Google Scholar
  6. 6.
    Duan HX, Wu YH (2011) Paracatadioptric camera calibration using sphere images. In: International conference on image processing, pp 649–652Google Scholar
  7. 7.
    Duan HX, Wu YH (2011) Unified imaging of geometric entities under catadioptric camera and camera calibration. J Comput-Aided Des Comput Graph 23:891–898Google Scholar
  8. 8.
    Duan HX, Wu YH (2012) A calibration method for paracatadioptric camera from sphere images. Pattern Recogn Lett 33:677–684CrossRefGoogle Scholar
  9. 9.
    Duan HX, Mei L, Shang YF, Hu CP (2014) Calibrating focal length for paracatadioptric camera from one circle image. In: International conference on computer vision theory and application, pp 56–63Google Scholar
  10. 10.
    Duan HX, Wu YH, Wang J, Song L, Liu N (2017) Fitting a cluster of line images under centeral catadioptric camera. Clust Comput 1–8Google Scholar
  11. 11.
    Geyer C, Daniilidis K (1999) Catadioptric camera calibration. In: International conference on computer vision, pp 398–404Google Scholar
  12. 12.
    Geyer C, Daniilidis K (2001) Catadioptric projective geometry. Int J Comput Vis 45:223–243CrossRefGoogle Scholar
  13. 13.
    Geyer C, Daniilidis K (2002) Paracatadioptric camera calibration. IEEE Trans Pattern Anal Mach Intell 24:687–695CrossRefGoogle Scholar
  14. 14.
    Habib A, Pullivelli A, Mitishita E, Ghanma M, Kim E (2006) Stability analysis of low-cost digital cameras for aerial mapping usin different georeferencing techniques. Photogramm Rec 21(113):29–43CrossRefGoogle Scholar
  15. 15.
    Hartley RI, Kang SB (2005) Parameter-free radial distortion correction with centre of distortion estimation. In: International conference on computer vision, pp 1834–1841Google Scholar
  16. 16.
    Harvey JE, Bogunovic D, Krywonos A (2003) Aberrations of diffracted wave fields: distortion. Appl Opt 42(7):1167–1174CrossRefGoogle Scholar
  17. 17.
    Maeda PY, Catrysse PB, Wandell BA (2005) Integrating lens design with digital camera simulation. In: SPIE electronic imagingGoogle Scholar
  18. 18.
    Mashita T, Iwai Y, Yachida M (2005) Calibration method for misaligned catadioptric. In: Workshop on OmnidirectionalVision, camera networks, and non-classical camerasGoogle Scholar
  19. 19.
    Semple JG, Kneebone GT (1998) Algebraic projective geometry. Claredon Press, OxfordzbMATHGoogle Scholar
  20. 20.
    Svoboda T, Pajdla T, Hlavac V (1997) Central panoramic cameras: geometry and design, Research report K335/97/147, Czech Technical University, Faculty of Electrical Engineering, Center for Machine PerceptionGoogle Scholar
  21. 21.
    Vandeportaele B, Cattoen M, Marthon P, Gurdjo P (2006) A new linear calibration method for paracatadioptric cameras. In: International conference on pattern recognition, pp 647–651Google Scholar
  22. 22.
    White N (1994) A tutorial on Gassmann-Cayley algebra. In: Invariant methods in discrete and computational geometry, pp 93–106Google Scholar
  23. 23.
    Wu FC, Duan FQ, Hu ZY, Wu YH (2008) A new linear algorithm for calibrating central catadioptric cameras. Pattern Recogn 41:3166–3172CrossRefGoogle Scholar
  24. 24.
    Wu YH, Hu ZY, Li YF (2014) Radial distortion invariance and lens evaluation under a single-optical-axis omnidirectional camera. Comput Vis Image Underst 126:11–27CrossRefGoogle Scholar
  25. 25.
    Ying XH, Hu ZY (2004) Catadioptric camera calibration using geometric invariants. IEEE Trans Pattern Anal Mach Intell 26:1260–1271CrossRefGoogle Scholar
  26. 26.
    Ying XH, Zha HB (2005) Simultaneously calibrating catadioptric camera and detecting line features using hough transform. In: International conference on intelligent robots and systems, pp 412–417Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Science CollegeEast China University of Science and TechnologyShanghaiChina
  2. 2.Cyber Physical System R & D CenterThe Third Research Institute of Ministry of Public SecurityShanghaiChina
  3. 3.Shanghai International Technology & Trade United Co., Ltd.ShanghaiChina
  4. 4.Shanghai Institute of Applied Physics, Chinese Academy of SciencesShanghaiChina

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